--- title: "mlx-serve vs ai-gateway" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ddalcu-mlx-serve-vs-ferro-labs-ai-gateway" tools: ["ddalcu-mlx-serve", "ferro-labs-ai-gateway"] --- # mlx-serve vs ai-gateway *GraphCanon updated Sep 20, 2026* ## Verdict Pick mlx-serve if focused on supporting Apple Silicon-powered macOS devices, mlx-serve provides a native and API-compatible inference service without requiring Python; pick ai-gateway if ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production environment. [mlx-serve](http://mlxserve.com/) reports 1.4k GitHub stars, 130 forks, and 54 open issues, last pushed Sep 19, 2026. [ai-gateway](https://docs.ferrolabs.ai) has 256 stars, 35 forks, and 68 open issues, last pushed Sep 10, 2026. Figures are from public GitHub metadata via [mlx-serve's repository](https://github.com/ddalcu/mlx-serve) and [ai-gateway's repository](https://github.com/ferro-labs/ai-gateway). | | [mlx-serve](/tools/ddalcu-mlx-serve.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Tagline | Native LLM inference server for Apple Silicon | Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls | | Stars | 1,418 | 256 | | Forks | 130 | 35 | | Open issues | 54 | 68 | | Language | Zig | Go | | Adopt for | Focused on supporting Apple Silicon-powered macOS devices, mlx-serve provides a native and API-compatible inference service without requiring Python. | ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production environment. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 - a permissive free software license | | Categories | Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mlx-serve](/tools/ddalcu-mlx-serve.md) | [ai-gateway](/tools/ferro-labs-ai-gateway.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 54 | 68 | | Stars delta | +1.1k (30d) | +37 (30d) | | Open issues delta | +51 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/ddalcu-mlx-serve/trust.md) | [trust report](/tools/ferro-labs-ai-gateway/trust.md) | ## Decision facts: mlx-serve - **Requirements:** Requires Apple Silicon-powered macOS devices to ensure optimal performance and compatibility. - **Adopt for:** Focused on supporting Apple Silicon-powered macOS devices, mlx-serve provides a native and API-compatible inference service without requiring Python. ## Decision facts: ai-gateway - **Adopt for:** ai-gateway from Ferro Labs supports over 30 LLMs with integrated caching, guardrails, A/B testing, and cost controls, making it ideal for managing multiple language models in a production environment. - **License detail:** Apache-2.0 - a permissive free software license ## Choose when ### Choose mlx-serve if… - mlx-serve is primarily Zig; ai-gateway is Go. - License: mlx-serve is MIT, ai-gateway is Apache-2.0. - Requirements: Requires Apple Silicon-powered macOS devices to ensure optimal performance and compatibility.. - Tags unique to mlx-serve: agent, anthropic-api, apple-silicon, deepseek-v4. - Use when your project requires running large language model (LLM) inferencing natively on Apple Silicon hardware. ### Choose ai-gateway if… - ai-gateway is primarily Go; mlx-serve is Zig. - License: ai-gateway is Apache-2.0, mlx-serve is MIT. - Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy. - Also covers Model Training. - When you need to integrate more than 30 different LLM services including OpenAI and Anthropic ## When NOT to use mlx-serve - Avoid if your infrastructure does not include devices with Apple Silicon chips, as it is specifically optimized for this architecture. - Do not use if you require cross-platform compatibility as mlx-serve targets macOS exclusively. - This tool might not be suitable if Python integration is crucial in your project. ## When NOT to use ai-gateway - If your project only involves one or two LLMs which does not necessitate the gateway's broad compatibility features - For small-scale projects that do not require comprehensive cost analysis tools - When custom integration for specific guardrails is required, as ai-gateway offers generalized settings ## Common questions ### What is the difference between mlx-serve and ai-gateway? mlx-serve: Native LLM inference server for Apple Silicon. ai-gateway: Unified AI Gateway for multiple LLMs with caching, guardrails, A/B testing, and cost controls. See the comparison table for live GitHub stats and shared categories. ### When should I choose mlx-serve over ai-gateway? Choose mlx-serve over ai-gateway when mlx-serve is primarily Zig; ai-gateway is Go; License: mlx-serve is MIT, ai-gateway is Apache-2.0; Requirements: Requires Apple Silicon-powered macOS devices to ensure optimal performance and compatibility.; Tags unique to mlx-serve: agent, anthropic-api, apple-silicon, deepseek-v4; Use when your project requires running large language model (LLM) inferencing natively on Apple Silicon hardware. ### When should I choose ai-gateway over mlx-serve? Choose ai-gateway over mlx-serve when ai-gateway is primarily Go; mlx-serve is Zig; License: ai-gateway is Apache-2.0, mlx-serve is MIT; Tags unique to ai-gateway: ai-gateway, litellm, llm-cost, llm-proxy; Also covers Model Training; When you need to integrate more than 30 different LLM services including OpenAI and Anthropic. ### When should I avoid mlx-serve? Avoid if your infrastructure does not include devices with Apple Silicon chips, as it is specifically optimized for this architecture. Do not use if you require cross-platform compatibility as mlx-serve targets macOS exclusively. This tool might not be suitable if Python integration is crucial in your project. ### When should I avoid ai-gateway? If your project only involves one or two LLMs which does not necessitate the gateway's broad compatibility features For small-scale projects that do not require comprehensive cost analysis tools When custom integration for specific guardrails is required, as ai-gateway offers generalized settings ### Is mlx-serve or ai-gateway more popular on GitHub? mlx-serve has more GitHub stars (1,418 vs 256). Stars measure visibility, not whether either tool fits your constraints. ### Are mlx-serve and ai-gateway open source? Yes - both are open-source projects on GitHub (mlx-serve: MIT, ai-gateway: Apache-2.0). ### Where can I find alternatives to mlx-serve or ai-gateway? GraphCanon lists graph-backed alternatives at [mlx-serve alternatives](/tools/ddalcu-mlx-serve/alternatives) and [ai-gateway alternatives](/tools/ferro-labs-ai-gateway/alternatives) ([mlx-serve markdown twin](/tools/ddalcu-mlx-serve/alternatives.md), [ai-gateway markdown twin](/tools/ferro-labs-ai-gateway/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/ddalcu-mlx-serve-vs-ferro-labs-ai-gateway.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mlx-serve or ai-gateway? mlx-serve: Very active. ai-gateway: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for mlx-serve and ai-gateway? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mlx-serve trust report](/tools/ddalcu-mlx-serve/trust); [ai-gateway trust report](/tools/ferro-labs-ai-gateway/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ddalcu-mlx-serve`](/api/graphcanon/graph?tool=ddalcu-mlx-serve) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_